test_test_model_multiple_t02_noBuf.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------0------------\n",
      "[{'penalty': 1000, 'num_r': 4, 'rate': 1.0, 'action_bound': 2, 'data_buf_size': 100, 'model': 'AR', 'id': '1', 't_factor': 0.2, 'dis': 100}, {'penalty': 1000, 'num_r': 4, 'rate': 2.0, 'action_bound': 2, 'data_buf_size': 100, 'model': 'AR', 'id': '2', 't_factor': 0.2, 'dis': 100}, {'penalty': 1000, 'num_r': 4, 'rate': 3.0, 'action_bound': 2, 'data_buf_size': 100, 'model': 'AR', 'id': '3', 't_factor': 0.2, 'dis': 100}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_02_nB/my_train_model_0-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_02_nB/my_train_model_0-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_02_nB/my_train_model_0-2000\n",
      "Initialization OK!----> user 3\n",
      "0:r:[-1.82965702 -3.94752605 -8.3769811 ],p:[0.3635054  0.87111373 2.56827862],op:[0.09233615 0.05375821 0.05082056],tr:[0.01887723 0.78338568 2.12991285],pr:[1.38593584 1.3993614  1.25139185],rev:[1.0076 2.0108 2.9898],dbuf:[1.14746739 2.62222772 3.92347841],ch:[4.08543229 3.91382589 4.08023211],ibuf:[0, 0, 0],rbuf:[2.48402059 0.75352844 6.        ]\n",
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      "3:r:[-1.82594659 -3.85519293 -8.34904453],p:[0.36260143 0.84525109 2.55987976],op:[0.09205213 0.05925938 0.05194506],tr:[0.01765465 0.81339095 2.15807024],pr:[1.38510259 1.37291383 1.23518082],rev:[1.0072 1.9944 2.9916],dbuf:[1.14579933 2.557715   3.90674363],ch:[3.9085449  4.10072491 4.09289474],ibuf:[0, 0, 0],rbuf:[0. 2. 1.]\n",
      "4:r:[-1.83287903 -3.8751002  -8.50242063],p:[0.36638805 0.85180639 2.60166066],op:[0.09151166 0.05551857 0.05070035],tr:[0.01688299 0.79730753 2.0994245 ],pr:[1.38812017 1.3741698  1.3037068 ],rev:[1.0063 1.9887 3.0255],dbuf:[1.14634952 2.57556286 3.99712327],ch:[4.06676658 4.09470655 3.97763759],ibuf:[0, 0, 0],rbuf:[3. 3. 2.]\n",
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      "98:r:[-1.84230251 -3.97070692 -8.42484965],p:[0.36760707 0.8769712  2.58885109],op:[0.09054544 0.0545649  0.05256313],tr:[0.01829196 0.80090273 2.08097236],pr:[1.38727385 1.39773306 1.29240838],rev:[1.0116 2.0227 3.0009],dbuf:[1.15749686 2.63454338 3.9275265 ],ch:[3.89109086 3.96531393 3.94186615],ibuf:[0, 0, 0],rbuf:[2.         3.         3.83510074]\n",
      "99:r:[-1.85965832 -3.94070917 -8.47001919],p:[0.3720868  0.87225624 2.59020868],op:[0.09077996 0.05333753 0.0538089 ],tr:[0.01905015 0.77417035 2.0968824 ],pr:[1.39169422 1.39311824 1.30305378],rev:[1.0186 1.9931 3.0363],dbuf:[1.167406   2.61190204 3.97748004],ch:[4.01482251 4.01975957 3.97231418],ibuf:[0, 0, 0],rbuf:[1.         2.85652914 9.89495479]\n",
      "---------1------------\n",
      "[{'penalty': 1000, 'num_r': 4, 'rate': 1.0, 'action_bound': 2, 'data_buf_size': 100, 'model': 'AR', 'id': '1', 't_factor': 0.2, 'dis': 100}, {'penalty': 1000, 'num_r': 4, 'rate': 2.0, 'action_bound': 2, 'data_buf_size': 100, 'model': 'AR', 'id': '2', 't_factor': 0.2, 'dis': 100}, {'penalty': 1000, 'num_r': 4, 'rate': 3.0, 'action_bound': 2, 'data_buf_size': 100, 'model': 'AR', 'id': '3', 't_factor': 0.2, 'dis': 100}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_02_nB/my_train_model_1-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_02_nB/my_train_model_1-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_02_nB/my_train_model_1-2000\n",
      "Initialization OK!----> user 3\n",
      "0:r:[-1.58155911 -4.48778355 -7.12285264],p:[0.2255605  1.16057362 1.99444023],op:[0.00972465 0.03854637 0.00734366],tr:[0.19356534 0.19838337 1.43616102],pr:[0.91183802 1.85764644 1.59267822],rev:[1.0132 1.9974 3.0096],dbuf:[1.38873603 2.61192945 3.89910609],ch:[4.01364754 4.02512321 4.12221476],ibuf:[0, 0, 0],rbuf:[2.         3.09782741 8.05575874]\n",
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      "96:r:[-1.52376682 -4.60346217 -7.24484552],p:[0.21434068 1.19163713 2.01887193],op:[0.00915637 0.03804653 0.00718107],tr:[0.17169975 0.19639324 1.41361781],pr:[0.90232654 1.87915301 1.60471276],rev:[0.9868 2.0167 2.9987],dbuf:[1.34596588 2.68011855 3.99092441],ch:[3.92198202 3.96211421 4.03948533],ibuf:[0, 0, 0],rbuf:[3.54044916 2.22216161 9.22316941]\n",
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      "98:r:[-1.55916001 -4.61228115 -7.36065717],p:[0.22251055 1.19556661 2.05234192],op:[0.0090177  0.03693034 0.00703397],tr:[0.197151   0.20903812 1.41660343],pr:[0.89936406 1.87963313 1.62025174],rev:[1.0107 2.0312 3.0167],dbuf:[1.37012939 2.68410906 4.05238173],ch:[4.23577791 4.05766214 3.97410945],ibuf:[0, 0, 0],rbuf:[2.10598251 2.23637884 2.10130082]\n",
      "99:r:[-1.587156   -4.5973992  -7.38935691],p:[0.22584067 1.1885319  2.07273988],op:[0.00985265 0.03878862 0.00706554],tr:[0.18469902 0.20690056 1.39127388],pr:[0.91597937 1.86880323 1.62880736],rev:[1.0097 2.0161 3.0005],dbuf:[1.3947117  2.6784477  4.03718259],ch:[3.98730457 4.07612331 3.92711461],ibuf:[0, 0, 0],rbuf:[1.59953062 2.29234247 5.37126622]\n",
      "---------2------------\n",
      "[{'penalty': 1000, 'num_r': 4, 'rate': 1.0, 'action_bound': 2, 'data_buf_size': 100, 'model': 'AR', 'id': '1', 't_factor': 0.2, 'dis': 100}, {'penalty': 1000, 'num_r': 4, 'rate': 2.0, 'action_bound': 2, 'data_buf_size': 100, 'model': 'AR', 'id': '2', 't_factor': 0.2, 'dis': 100}, {'penalty': 1000, 'num_r': 4, 'rate': 3.0, 'action_bound': 2, 'data_buf_size': 100, 'model': 'AR', 'id': '3', 't_factor': 0.2, 'dis': 100}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_02_nB/my_train_model_2-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_02_nB/my_train_model_2-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_02_nB/my_train_model_2-2000\n",
      "Initialization OK!----> user 3\n",
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      "98:r:[ -1.75807468  -6.12228763 -10.81609864],p:[0.33563116 1.20457394 2.7322914 ],op:[0.0780868  0.90520073 1.26770859],tr:[0.01518882 0.22676354 2.15588711],pr:[1.26883606 2.5198421  2.5198421 ],rev:[0.9947 1.9999 3.0216],dbuf:[1.16329845 2.37842288 3.52012331],ch:[3.96522348 3.96124684 4.08144745],ibuf:[0, 0, 0],rbuf:[0.91273027 3.36942195 9.55673954]\n",
      "99:r:[ -1.7670433   -6.13125366 -10.87601607],p:[0.3393218  1.20599149 2.74370164],op:[0.07559806 0.90494906 1.25629832],tr:[0.01601049 0.22882027 2.1576165 ],pr:[1.26794908 2.5198421  2.51984209],rev:[0.9985 2.0058 3.0231],dbuf:[1.17150446 2.3867157  3.59502017],ch:[4.15864183 4.06566748 4.07350834],ibuf:[0, 0, 0],rbuf:[0.         2.71692044 4.59471418]\n",
      "---------3------------\n",
      "[{'penalty': 1000, 'num_r': 4, 'rate': 1.0, 'action_bound': 2, 'data_buf_size': 100, 'model': 'AR', 'id': '1', 't_factor': 0.2, 'dis': 100}, {'penalty': 1000, 'num_r': 4, 'rate': 2.0, 'action_bound': 2, 'data_buf_size': 100, 'model': 'AR', 'id': '2', 't_factor': 0.2, 'dis': 100}, {'penalty': 1000, 'num_r': 4, 'rate': 3.0, 'action_bound': 2, 'data_buf_size': 100, 'model': 'AR', 'id': '3', 't_factor': 0.2, 'dis': 100}]\n",
      "INFO:tensorflow:Restoring parameters from model_M_02_nB/my_train_model_3-2000\n",
      "Initialization OK!----> user 1\n",
      "INFO:tensorflow:Restoring parameters from model_M_02_nB/my_train_model_3-2000\n",
      "Initialization OK!----> user 2\n",
      "INFO:tensorflow:Restoring parameters from model_M_02_nB/my_train_model_3-2000\n",
      "Initialization OK!----> user 3\n",
      "0:r:[-1.51795486 -6.8207746  -7.63573311],p:[0.26880945 1.38236374 2.03491487],op:[0.02195101 0.61763626 0.02065599],tr:[2.69041830e-01 4.13301795e-13 1.25716509e+00],pr:[0.97770729 2.5198421  1.77412227],rev:[1.014  2.0163 3.0005],dbuf:[1.17054242 3.52596825 4.40573923],ch:[4.0671791  4.15225226 4.05703318],ibuf:[0, 0, 0],rbuf:[1.05035072 0.4404737  2.        ]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "22:r:[-1.51770973 -6.6942163  -7.50268137],p:[0.2688288  1.38324898 2.00147879],op:[0.02193746 0.61675102 0.02155703],tr:[2.70755312e-01 1.77818617e-13 1.24280719e+00],pr:[0.97643122 2.5198421  1.75650348],rev:[1.013  2.0155 2.9659],dbuf:[1.17022152 3.36777037 4.32076215],ch:[4.03296965 3.72947979 4.04836982],ibuf:[0, 0, 0],rbuf:[2. 3. 4.]\n",
      "23:r:[-1.51047207 -6.67124026 -7.6335435 ],p:[0.26850365 1.36048423 2.03114227],op:[0.02214594 0.63951577 0.02224893],tr:[2.59704998e-01 1.57475514e-13 1.26408269e+00],pr:[0.9793398  2.5198421  1.76680913],rev:[1.0031 1.9948 2.9975],dbuf:[1.16146611 3.33905032 4.40845138],ch:[3.92690407 4.00241291 3.99601407],ibuf:[0, 0, 0],rbuf:[0.         4.4404737  4.61910317]\n",
      "24:r:[-1.45843023 -6.75421349 -7.67601366],p:[0.25459652 1.37544829 2.05305079],op:[0.02308909 0.62455171 0.02138688],tr:[2.56484606e-01 5.31358600e-13 1.24009071e+00],pr:[0.96852331 2.5198421  1.79185612],rev:[0.9806 2.0106 2.9999],dbuf:[1.12882377 3.44276686 4.40892289],ch:[4.14790631 3.90227623 3.91075072],ibuf:[0, 0, 0],rbuf:[0.         2.4801579  0.02536627]\n",
      "25:r:[-1.47385257 -6.69717012 -7.73237187],p:[0.25778923 1.3725823  2.05182132],op:[0.02240387 0.6274177  0.02440485],tr:[2.59764258e-01 9.46939856e-13 1.25674751e+00],pr:[0.96996126 2.5198421  1.78368869],rev:[0.9909 2.0031 3.0054],dbuf:[1.14183295 3.37146264 4.47489943],ch:[4.09362532 4.06693795 3.86635374],ibuf:[0, 0, 0],rbuf:[0. 1. 5.]\n",
      "26:r:[-1.51932765 -6.65160331 -7.75196063],p:[0.26953068 1.35906445 2.08081737],op:[0.02218168 0.64093555 0.01958329],tr:[2.66115033e-01 1.63060015e-13 1.22267234e+00],pr:[0.98037664 2.5198421  1.81619231],rev:[1.0125 1.9879 3.0086],dbuf:[1.16987866 3.31450414 4.43894913],ch:[3.9463235  3.89506338 3.8598572 ],ibuf:[0, 0, 0],rbuf:[1.05262504 5.2817369  1.        ]\n"
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      "27:r:[-1.50212084 -6.63476131 -7.85503533],p:[0.26515722 1.34197801 2.08658428],op:[0.02248576 0.65802199 0.0210713 ],tr:[2.63959152e-01 2.02731697e-13 1.25892630e+00],pr:[0.97604959 2.5198421  1.80543828],rev:[1.0034 1.9762 3.0324],dbuf:[1.15854359 3.29345163 4.5496552 ],ch:[4.05494376 4.04834138 3.99066025],ibuf:[0, 0, 0],rbuf:[1.16209636 7.2023685  1.56349918]\n",
      "28:r:[-1.5006734  -6.6638851  -7.60830809],p:[0.26540275 1.35980269 2.02816112],op:[0.02287602 0.64019731 0.02009451],tr:[2.69973173e-01 2.23783495e-13 1.24597411e+00],pr:[0.97377946 2.5198421  1.768509  ],rev:[1.002  1.994  2.9843],dbuf:[1.1551448  3.32985637 4.38974602],ch:[4.06521008 4.04279359 4.09371408],ibuf:[0, 0, 0],rbuf:[0.0706315  3.3214211  7.34674339]\n",
      "29:r:[-1.52672927 -6.81424952 -7.77458489],p:[0.27181957 1.39431558 2.07431079],op:[0.02225913 0.60568442 0.01928088],tr:[2.65827723e-01 1.20754347e-13 1.22649218e+00],pr:[0.97917738 2.5198421  1.80554489],rev:[1.0089 2.0222 3.0027],dbuf:[1.17321486 3.51781191 4.48425193],ch:[4.04963896 3.87383706 3.91781389],ibuf:[0, 0, 0],rbuf:[2. 1. 5.]\n",
      "30:r:[-1.47749124 -6.64123158 -7.60436256],p:[0.25804241 1.3591119  2.02107611],op:[0.02235416 0.6408881  0.02176423],tr:[2.57192014e-01 7.26181422e-13 1.26169168e+00],pr:[0.97309089 2.5198421  1.76510439],rev:[0.9958 1.9941 2.9925],dbuf:[1.14587262 3.30153948 4.39835236],ch:[4.07699555 4.08141729 4.20382382],ibuf:[0, 0, 0],rbuf:[2.01108838 3.         1.3184717 ]\n",
      "31:r:[-1.50063639 -6.63971075 -7.6214243 ],p:[0.26566274 1.33839968 2.02728037],op:[0.02232178 0.66160032 0.02210117],tr:[2.59829143e-01 2.11942285e-13 1.25325955e+00],pr:[0.97683355 2.5198421  1.77340347],rev:[0.9976 1.9756 2.9943],dbuf:[1.15583418 3.29963844 4.40332653],ch:[3.95765893 4.08684975 3.93804801],ibuf:[0, 0, 0],rbuf:[ 1.23044034  5.         10.48119228]\n",
      "32:r:[-1.48568788 -6.76989726 -7.62010264],p:[0.26195459 1.37351564 2.0306144 ],op:[0.02259788 0.62648436 0.02112145],tr:[2.57764898e-01 2.28402964e-13 1.25795721e+00],pr:[0.97449951 2.5198421  1.76862981],rev:[0.9912 2.0027 2.9946],dbuf:[1.14572866 3.46237158 4.39578868],ch:[4.03521132 3.98986371 4.07282665],ibuf:[0, 0, 0],rbuf:[1.05247421 1.         6.11985805]\n",
      "33:r:[-1.53324055 -6.65928599 -7.58900585],p:[0.27254841 1.36308557 2.0223737 ],op:[0.02199969 0.63691443 0.02194406],tr:[2.64833048e-01 3.91079753e-14 1.26139964e+00],pr:[0.98324915 2.5198421  1.77577782],rev:[1.0179 2.0006 3.0045],dbuf:[1.18018044 3.32410748 4.37546289],ch:[3.90042165 4.11888735 3.99809799],ibuf:[0, 0, 0],rbuf:[3.         2.         6.39073709]\n",
      "34:r:[-1.46644126 -6.58761657 -7.64699212],p:[0.25708423 1.35750443 2.03729981],op:[0.02309194 0.64249557 0.02152243],tr:[2.62163783e-01 2.29153623e-13 1.25090430e+00],pr:[0.97018209 2.5198421  1.77552087],rev:[0.9867 1.9912 2.9934],dbuf:[1.13261115 3.23452072 4.41168455],ch:[4.02419759 4.00074814 3.94010154],ibuf:[0, 0, 0],rbuf:[2.00705099 4.         3.14737002]\n",
      "35:r:[-1.542432   -6.68521197 -7.84381422],p:[0.274437   1.34630698 2.08238479],op:[0.02231416 0.65369302 0.02001251],tr:[2.70206112e-01 5.70340617e-13 1.25224176e+00],pr:[0.98271861 2.5198421  1.80703154],rev:[1.0222 1.9797 3.029 ],dbuf:[1.18616209 3.35651496 4.54877452],ch:[4.01530042 3.95897475 4.03308991],ibuf:[0, 0, 0],rbuf:[1.54805178 1.         6.43035328]\n",
      "36:r:[-1.48950878 -6.49895053 -7.66942579],p:[0.26109636 1.33012075 2.0449911 ],op:[0.02250164 0.66987925 0.02083726],tr:[2.62096602e-01 5.50295913e-13 1.25166251e+00],pr:[0.9744005  2.5198421  1.78097356],rev:[1.0002 1.9708 3.0009],dbuf:[1.15289095 3.12368816 4.42221135],ch:[4.11298495 3.86328495 3.95538142],ibuf:[0, 0, 0],rbuf:[1.43549906 3.9206316  5.27671511]\n",
      "37:r:[-1.49597209 -6.55796405 -7.7605847 ],p:[0.26450401 1.33839053 2.06125581],op:[0.0226163  0.66160947 0.01873918],tr:[2.63318496e-01 2.26816852e-13 1.25142505e+00],pr:[0.97156315 2.5198421  1.78474714],rev:[0.9933 1.9779 3.0069],dbuf:[1.15216435 3.19745506 4.50074341],ch:[4.11230907 3.98985857 4.04256206],ibuf:[0, 0, 0],rbuf:[2.09815061 5.3611053  3.28295221]\n",
      "38:r:[-1.51235618 -6.69655317 -7.86668219],p:[0.26830912 1.35592715 2.10365985],op:[0.02246595 0.64407285 0.01802814],tr:[2.56071233e-01 4.90420251e-13 1.21999488e+00],pr:[0.97998808 2.5198421  1.81813471],rev:[1.0011 1.988  3.0103],dbuf:[1.16350754 3.37069146 4.52913278],ch:[3.96934278 4.14937625 3.85133188],ibuf:[0, 0, 0],rbuf:[1.09381762 4.4007895  6.83516085]\n",
      "39:r:[-1.51946889 -6.69484112 -7.61102899],p:[0.26932792 1.37669756 2.03846102],op:[0.02233218 0.62330244 0.02117812],tr:[2.66379575e-01 3.09848353e-13 1.22696285e+00],pr:[0.97798728 2.5198421  1.78349441],rev:[1.009  2.009  2.9787],dbuf:[1.17018586 3.3685514  4.36468837],ch:[3.94366462 4.0855783  3.90703491],ibuf:[0, 0, 0],rbuf:[2.01032117 1.         2.74079118]\n",
      "40:r:[-1.52595615 -6.81756056 -7.6792077 ],p:[0.27137872 1.38078913 2.04589761],op:[0.02261992 0.61921087 0.02086541],tr:[2.67983595e-01 3.83677692e-13 1.26982272e+00],pr:[0.97912069 2.5198421  1.7786777 ],rev:[1.0099 2.0128 3.0159],dbuf:[1.17244862 3.5219507  4.43210207],ch:[3.96431755 3.81978019 4.03133488],ibuf:[0, 0, 0],rbuf:[0.09969929 7.8809474  2.        ]\n",
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      "42:r:[-1.49818993 -6.7367685  -7.7158584 ],p:[0.26522934 1.39433489 2.05206636],op:[0.02255594 0.60566511 0.02228974],tr:[2.58680452e-01 1.24133418e-14 1.26082198e+00],pr:[0.97648972 2.5198421  1.78407536],rev:[0.9955 2.0269 3.0114],dbuf:[1.15327422 3.42096063 4.45893273],ch:[3.99547363 4.10242026 4.02900336],ibuf:[0, 0, 0],rbuf:[1.         3.3214211  5.83300294]\n",
      "43:r:[-1.50359677 -6.73909721 -7.6392166 ],p:[0.26505087 1.38124951 2.04000441],op:[0.02215869 0.61875049 0.0195689 ],tr:[2.67331006e-01 4.98552534e-13 1.24943069e+00],pr:[0.97439093 2.5198421  1.77780211],rev:[1.0075 2.0116 2.9974],dbuf:[1.16147206 3.42387152 4.40008747],ch:[4.07902185 3.97923342 3.9907666 ],ibuf:[0, 0, 0],rbuf:[4.        0.        6.9759562]\n",
      "44:r:[-1.49821546 -6.66655116 -7.55136673],p:[0.264657   1.36928605 2.02559552],op:[0.02274769 0.63071395 0.02085967],tr:[2.61610441e-01 8.60325195e-13 1.22835516e+00],pr:[0.97642666 2.5198421  1.78067558],rev:[0.9987 2.0057 2.9775],dbuf:[1.15425759 3.33318895 4.32307045],ch:[3.9716357  3.91569291 3.91510117],ibuf:[0, 0, 0],rbuf:[0.         4.         6.02532821]\n"
     ]
    }
   ],
   "source": [
    "from mec_env_var import *\n",
    "from helper import *\n",
    "import tensorflow as tf\n",
    "import ipdb as pdb\n",
    "import time\n",
    "\n",
    "for k in range(10):\n",
    "    \n",
    "    tf.reset_default_graph()\n",
    "    \n",
    "    print('---------' + str(k) + '------------')\n",
    "    \n",
    "    MAX_EPISODE = 100\n",
    "    MAX_EPISODE_LEN = 10000\n",
    "\n",
    "    NUM_T = 1\n",
    "    NUM_R = 4\n",
    "    SIGMA2 = 1e-9\n",
    "\n",
    "    t_factor = 0.2\n",
    "    noise_sigma = 0.12\n",
    "\n",
    "    config = {'state_dim':3, 'action_dim':2};\n",
    "    train_config = {'minibatch_size':64, 'actor_lr':0.0001, 'tau':0.001, \n",
    "                    'critic_lr':0.001, 'gamma':0.99, 'buffer_size':250000, \n",
    "                    'random_seed':int(time.clock()*1000%1000), 'noise_sigma':noise_sigma, 'sigma2':SIGMA2}\n",
    "    \n",
    "#     rate = 3.0\n",
    "    res_path = 't_M_02_nB/'\n",
    "    model_path = 'model_M_02_nB/my_train_model_'+str(k)+'-2000'\n",
    "    meta_path = model_path+'.meta'    \n",
    "    user_config = [{'id':'1', 'model':'AR', 'num_r':NUM_R, 'rate':1.0, 'dis':100, 'action_bound':2, \n",
    "                    'data_buf_size':100, 't_factor':t_factor, 'penalty':1000},\n",
    "                   {'id':'2', 'model':'AR', 'num_r':NUM_R, 'rate':2.0, 'dis':100, 'action_bound':2, \n",
    "                    'data_buf_size':100, 't_factor':t_factor, 'penalty':1000},\n",
    "                   {'id':'3', 'model':'AR', 'num_r':NUM_R, 'rate':3.0, 'dis':100, 'action_bound':2, \n",
    "                    'data_buf_size':100, 't_factor':t_factor, 'penalty':1000}]\n",
    "    \n",
    "    print(user_config)\n",
    "    # 0. initialize the session object\n",
    "    sess = tf.Session() \n",
    "\n",
    "    # gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.2)\n",
    "    # sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) \n",
    "\n",
    "    # 1. include all user in the system according to the user_config\n",
    "    user_list = [];\n",
    "    for info in user_config:\n",
    "        info.update(config)\n",
    "        info['model_path'] = model_path\n",
    "        info['meta_path'] = info['model_path']+'.meta'\n",
    "        user_list.append(MecTermLD(sess, info, train_config))\n",
    "        print('Initialization OK!----> user ' + info['id'])\n",
    "\n",
    "    # 2. create the simulation env\n",
    "    env = MecSvrEnv(user_list, NUM_R, SIGMA2, MAX_EPISODE_LEN)\n",
    "\n",
    "#     sess.run(tf.global_variables_initializer())\n",
    "\n",
    "#     env.init_target_network()\n",
    "\n",
    "    # #Create a saver object which will save all the variables\n",
    "    # saver = tf.train.Saver() \n",
    "\n",
    "    res_r = []\n",
    "    res_p = []\n",
    "    res_b = []\n",
    "    res_o = []\n",
    "    res_d = []\n",
    "    # 3. start to explore for each episode\n",
    "    for i in range(MAX_EPISODE):\n",
    "\n",
    "        cur_init_ds_ep = env.reset(isTrain=False)\n",
    "        \n",
    "        cur_r_ep = np.zeros(len(user_list))\n",
    "        cur_p_ep = np.zeros(len(user_list))\n",
    "        cur_op_ep = np.zeros(len(user_list))\n",
    "        cur_ts_ep = np.zeros(len(user_list))\n",
    "        cur_ps_ep = np.zeros(len(user_list))\n",
    "        cur_rs_ep = np.zeros(len(user_list))\n",
    "        cur_ds_ep = np.zeros(len(user_list))\n",
    "        cur_ch_ep = np.zeros(len(user_list))\n",
    "        cur_of_ep = np.zeros(len(user_list))\n",
    "\n",
    "        for j in range(MAX_EPISODE_LEN):\n",
    "\n",
    "            # first try to transmit from current state\n",
    "            [cur_r, done, cur_p, cur_op, temp, cur_ts, cur_ps, cur_rs, cur_ds, cur_ch, cur_of] = env.step_transmit()\n",
    "\n",
    "            cur_r_ep += cur_r\n",
    "            cur_p_ep += cur_p\n",
    "            cur_op_ep += cur_op\n",
    "            cur_ts_ep += cur_ts\n",
    "            cur_ps_ep += cur_ps\n",
    "            cur_rs_ep += cur_rs\n",
    "            cur_ds_ep += cur_ds\n",
    "            cur_ch_ep += cur_ch\n",
    "            cur_of_ep += cur_of\n",
    "\n",
    "            if done:\n",
    "                res_r.append(cur_r_ep/MAX_EPISODE_LEN)\n",
    "                res_p.append(cur_p_ep/MAX_EPISODE_LEN)\n",
    "                res_b.append(cur_ds_ep/MAX_EPISODE_LEN)\n",
    "                res_o.append(cur_of_ep/MAX_EPISODE_LEN)\n",
    "                res_d.append(cur_ds)\n",
    "                print('%d:r:%s,p:%s,op:%s,tr:%s,pr:%s,rev:%s,dbuf:%s,ch:%s,ibuf:%s,rbuf:%s' % (i, cur_r_ep/MAX_EPISODE_LEN, cur_p_ep/MAX_EPISODE_LEN, cur_op_ep/MAX_EPISODE_LEN, cur_ts_ep/MAX_EPISODE_LEN, cur_ps_ep/MAX_EPISODE_LEN, cur_rs_ep/MAX_EPISODE_LEN, cur_ds_ep/MAX_EPISODE_LEN, cur_ch_ep/MAX_EPISODE_LEN, cur_init_ds_ep, cur_ds))\n",
    "\n",
    "    name = res_path+'test_1000_' + time.strftime(\"%b_%d_%Y_%H_%M_%S\", time.localtime(time.time()))\n",
    "    np.savez(name, res_r, res_p, res_b, res_o, res_d)\n",
    "    \n",
    "    sess.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "name = res_path+'/test_1000_' + time.strftime(\"%b_%d_%Y_%H_%M_%S\", time.localtime(time.time()))\n",
    "# print(name)\n",
    "np.savez(name, res_r, res_p, res_b, res_o, res_d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph = tf.get_default_graph()\n",
    "# variable_names = [v.name for v in tf.trainable_variables()]\n",
    "# values = sess.run(variable_names)\n",
    "# tf.trainable_variables()\n",
    "\n",
    "inputs = graph.get_tensor_by_name(\"input_1/X\"+\":0\")\n",
    "scaled_out = graph.get_tensor_by_name(\"output_1\"+\":0\")\n",
    "print sess.run(scaled_out, feed_dict={inputs: [[7.22254768,3.9729721,0.7729721]]})\n",
    "#Create a saver object which will save all the variables\n",
    "saver = tf.train.Saver()\n",
    " \n",
    "#Now, save the graph\n",
    "saver.save(sess, 'model/my_train_model',global_step=2000)\n",
    "\n",
    "# user_list[0].agent.actor.predict([[7.22254768,3.9729721,0.7729721]]) # [[0.63916105, 0.20230901]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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